CN109583394A - The recognition methods of vehicle tyre number and special equipment in highway tolling system - Google Patents
The recognition methods of vehicle tyre number and special equipment in highway tolling system Download PDFInfo
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- CN109583394A CN109583394A CN201811479510.6A CN201811479510A CN109583394A CN 109583394 A CN109583394 A CN 109583394A CN 201811479510 A CN201811479510 A CN 201811479510A CN 109583394 A CN109583394 A CN 109583394A
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- 238000000034 method Methods 0.000 title claims abstract description 13
- 238000010191 image analysis Methods 0.000 claims abstract description 22
- 238000001514 detection method Methods 0.000 claims description 8
- 238000013527 convolutional neural network Methods 0.000 claims description 7
- 238000004458 analytical method Methods 0.000 claims description 4
- 239000000284 extract Substances 0.000 claims description 4
- 239000011324 bead Substances 0.000 claims description 3
- 238000000605 extraction Methods 0.000 claims description 3
- 239000000203 mixture Substances 0.000 claims description 3
- 230000000994 depressogenic effect Effects 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 238000007689 inspection Methods 0.000 description 1
- 238000013517 stratification Methods 0.000 description 1
Classifications
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V20/00—Scenes; Scene-specific elements
- G06V20/50—Context or environment of the image
- G06V20/56—Context or environment of the image exterior to a vehicle by using sensors mounted on the vehicle
- G06V20/58—Recognition of moving objects or obstacles, e.g. vehicles or pedestrians; Recognition of traffic objects, e.g. traffic signs, traffic lights or roads
- G06V20/584—Recognition of moving objects or obstacles, e.g. vehicles or pedestrians; Recognition of traffic objects, e.g. traffic signs, traffic lights or roads of vehicle lights or traffic lights
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/04—Architecture, e.g. interconnection topology
- G06N3/045—Combinations of networks
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V10/00—Arrangements for image or video recognition or understanding
- G06V10/40—Extraction of image or video features
- G06V10/46—Descriptors for shape, contour or point-related descriptors, e.g. scale invariant feature transform [SIFT] or bags of words [BoW]; Salient regional features
- G06V10/462—Salient features, e.g. scale invariant feature transforms [SIFT]
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- G—PHYSICS
- G07—CHECKING-DEVICES
- G07B—TICKET-ISSUING APPARATUS; FARE-REGISTERING APPARATUS; FRANKING APPARATUS
- G07B15/00—Arrangements or apparatus for collecting fares, tolls or entrance fees at one or more control points
- G07B15/06—Arrangements for road pricing or congestion charging of vehicles or vehicle users, e.g. automatic toll systems
Abstract
The invention belongs to road pricing system technical fields, axle tire quantity recognition methods especially in highway tolling system, it acquires tire boss shape information by video camera to determine Vehicle Axles tire quantity, and sends axle tire quantity information in highway tolling system.Axle tire quantity identifying system in highway tolling system is provided simultaneously, is made of video camera, light compensating lamp, wagon detector, tire sensing device, image analysis apparatus, and wagon detector is mounted in vehicle course, and is connected with video camera；Tire sensing device is mounted in vehicle course and connect with video camera；Video camera is connected with image analysis apparatus.
Description
Technical field
The invention belongs to the recognition methods of vehicle tyre number and special equipments in highway tolling system.
Background technique
Highway tolling system is charged by truck vehicle weight at present, for different automobile types, there is different load-carryings
Limitation, the correct tire quantity for identifying the every axis of vehicle become the important evidence for judging vehicle.
The identification every axis tire number of vehicle relies primarily on pressure (piezoelectricity) sensor (the i.e. axle identification being arranged in rows at this stage
Device) it is identified, axle tire quantity is different, and the number of sensors rolled when passing through Wheel axle identifier is different, so can
To judge the tire quantity of axle according to the number of sensors rolled, the reliability and discrimination of this recognition methods be not high,
Mistake is easily identified for the tire quantity of partial width.
Axle tire number is quick and precisely identified in highway tolling system, correctly judges vehicle, is to improve current effect
The effective way of rate.
Summary of the invention
It is an object of the present invention to provide the recognition methods and equipment of vehicle tyre number in a kind of highway tolling system, with fast
Speed accurately identifies vehicle, improves traffic efficiency.
It is divided into recessed and protrusion among the wheel hub of lorry, when the every side of axle is single tire, wheel hub center protrusion is two-wheel
It when tire, is centrally depressed, and does not make an exception.
It is provided a kind of by identification wheel hub according to the rule of the above axle tire quantity using image recognition analysis technology
Shape determines axle tire quantitative approach and equipment.
The technical scheme adopted by the invention is that: axle tire quantity recognition methods in highway tolling system passes through
Video camera acquires tire boss shape information to determine axle tire quantity, and sends high speed public affairs for axle tire quantity information
Road charging system.
The recognition methods of axle tire quantity, first claps vehicle tyre in the highway tolling system
According to；Image procossing, feature extraction, feature identification are carried out to capture pictures by image analysis apparatus；Further according to the wheel hub of identification
Feature determines single, double tire；Finally highway tolling system is sent by single, double tire information.
Axle tire quantity recognition methods in the highway tolling system is realized in accordance with the following steps:
(1) vehicle detection apparatus is installed in vehicle travel line, after vehicle reaches detection zone, wagon detector is to figure
As analytical equipment issues vehicle arriving signal；
(2) the tire sensing device being mounted in vehicle travel line is detected and is taken pictures triggering after wheel to video camera sending
Signal, video capture tire image simultaneously send image analysis apparatus to；
(3) image analysis apparatus extracts the feature of bead seat using depth convolutional neural networks, analysis wheel hub center
Concaveconvex shape and obtain tire be single tire or twin tires；And it is uploaded to charging system.
Axle tire number identifying system in the highway tolling system is by video camera, light compensating lamp, vehicle inspection
Survey device, tire sensing device, image analysis apparatus composition, and wagon detector is mounted in vehicle course, and with camera shooting
Machine is connected；Tire sensing device is mounted on vehicle travel lane and connect with video camera；Video camera and image analysis apparatus phase
Connection.
The identifying system, image analysis apparatus obtain module and feature recognition module structure by image collection module, feature
At；And described image obtains module, for obtaining the image of wheel hub to be identified；The feature obtains module, for will it is described to
The image of identification wheel hub obtains hubless feature by depth convolutional neural networks；The feature recognition module, according to hubless feature,
Judge the tire number of axle.
Detailed description of the invention
Fig. 1 is the layout of equipment of the invention；
Fig. 2-1,2-2 are respectively single tire hubless feature photo and binary image；
Fig. 3-1,3-2 are respectively twin tires hubless feature photo and binary image；
Fig. 4 is working principle diagram of the present invention.
Specific embodiment
As shown in Figure 1, this equipment is filled by video camera 2, light compensating lamp, wagon detector 4, tire sensing device 3, image analysis
1 equal composition is set, the arrangement of equipment such as Fig. 1 is as shown.
After vehicle reaches 4 detection zone of wagon detector during traveling, wagon detector 4 is to image analysis apparatus 1
Vehicle arriving signal is issued, tire sensing device 3 can issue trigger signal of taking pictures to video camera 2 after detecting wheel, candid photograph
Image sends image analysis apparatus 1 to, and image analysis apparatus 1 extracts the feature of wheel hub, analysis using depth convolutional neural networks
The concaveconvex shape of wheel hub and obtain tire quantity (single tire or twin tires), such as the boss shape that Fig. 2 is single tire, Fig. 3 is
The boss shape of twin tires, when vehicle leaves 4 detection zone of wagon detector, image analysis apparatus 1 is according to the signal of detector
Variation determines after vehicle sails out of detection zone completely, is just uploaded the axle tire quantity of identification by PORT COM.
Image analysis apparatus 1 has the function of image procossing, feature extraction, feature identification etc., can be to the hub for vehicle wheel of candid photograph
Image extracts the feature of wheel hub using depth convolutional neural networks, and feature is carried out with stratification feature from bottom to high-rise spy
Sign is extracted.By the feature representation of learning hierarchy, feature is carried out to be automatically learned layer from bottom to high-rise description
The character representation of secondaryization, improves the discrimination and accuracy of identification of the feature of wheel hub, and system flow chart is as shown in Figure 4.
Claims (5)
1. the feature of bead seat is extracted using depth convolutional neural networks by video capture vehicle tyre image, analysis wheel
The concaveconvex shape in hub center and obtain the every side of axle be single tire or twin tires.
2. axle tire quantity recognition methods in highway tolling system according to claim 1, it is characterized in that first
It takes pictures to vehicle tyre；Image procossing, feature extraction, feature knowledge are carried out to capture pictures by image analysis apparatus (1)
Not；Single, double tire is determined further according to the hubless feature of identification；Finally expressway tol lcollection system is sent by single, double tire information
In system.
3. axle tire quantity recognition methods in highway tolling system according to claim 2, it is characterized in that according to
Following steps are realized:
(1) wagon detector (4) are installed in vehicle course, after vehicle reaches detection zone, coil type wagon detector
(4) vehicle arriving signal is issued to image analysis apparatus (1)；
(2) the tire sensing device (3) being mounted in vehicle course, which detects, issues touching of taking pictures to video camera (2) after wheel
It signals, video camera (2) captures image and sends image analysis apparatus (1) to；
(3) image analysis apparatus (1) extracts the feature of bead seat using depth convolutional neural networks, analyzes the concave-convex of wheel hub
Shape and obtain tire be single tire or twin tires；
(4) after vehicle leaves wagon detector detection zone, image analysis apparatus (1) can be by the axle tire quantity result of identification
It is uploaded in highway tolling system by PORT COM.
4. realize axle tire quantity recognition methods in highway tolling system described in claim 1-3, it is characterized in that by
Video camera (2), light compensating lamp, wagon detector (4), tire sensing device (3), image analysis apparatus (1) composition, and vehicle detection
Device (4) is mounted in vehicle course, and is connect with video camera (2)；Tire sensing device (3) is mounted on vehicle course
It is upper to be connect with video camera (2)；Video camera (2) is connect with image analysis apparatus in charging control system (1).
5. according to claim identifying system, it is characterized in that image analysis apparatus (1) obtains module by image collection module, feature
It is constituted with feature recognition module；And
Described image obtains module, for obtaining the image of wheel hub to be identified；
The feature obtains module, for the image of the wheel hub to be identified to be obtained wheel hub spy by depth convolutional neural networks
Sign；
The feature recognition module determines the axle tire quantity of the vehicle according to the hubless feature.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201811479510.6A CN109583394B (en) | 2018-12-05 | 2018-12-05 | Method and special equipment for identifying number of vehicle tires in expressway toll collection system |
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|---|---|---|---|
| CN201811479510.6A CN109583394B (en) | 2018-12-05 | 2018-12-05 | Method and special equipment for identifying number of vehicle tires in expressway toll collection system |
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| CN109583394A true CN109583394A (en) | 2019-04-05 |
| CN109583394B CN109583394B (en) | 2024-01-12 |
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| CN111179604A (en) * | 2020-01-16 | 2020-05-19 | 苏州朗为控制技术有限公司 | Vehicle type recognition method |
| CN111275008A (en) * | 2020-02-24 | 2020-06-12 | 浙江大华技术股份有限公司 | Method and device for detecting abnormality of target vehicle, storage medium, and electronic device |
| CN111325146A (en) * | 2020-02-20 | 2020-06-23 | 吉林省吉通信息技术有限公司 | Truck type and axle type identification method and system |
| CN116453075A (en) * | 2023-06-14 | 2023-07-18 | 山东省科学院海洋仪器仪表研究所 | Axle identification method and system based on image processing |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN111179604A (en) * | 2020-01-16 | 2020-05-19 | 苏州朗为控制技术有限公司 | Vehicle type recognition method |
| CN111325146A (en) * | 2020-02-20 | 2020-06-23 | 吉林省吉通信息技术有限公司 | Truck type and axle type identification method and system |
| CN111325146B (en) * | 2020-02-20 | 2021-06-04 | 吉林省吉通信息技术有限公司 | Truck type and axle type identification method and system |
| CN111275008A (en) * | 2020-02-24 | 2020-06-12 | 浙江大华技术股份有限公司 | Method and device for detecting abnormality of target vehicle, storage medium, and electronic device |
| CN111275008B (en) * | 2020-02-24 | 2024-01-16 | 浙江大华技术股份有限公司 | Method and device for detecting abnormality of target vehicle, storage medium and electronic device |
| CN116453075A (en) * | 2023-06-14 | 2023-07-18 | 山东省科学院海洋仪器仪表研究所 | Axle identification method and system based on image processing |
| CN116453075B (en) * | 2023-06-14 | 2023-09-08 | 山东省科学院海洋仪器仪表研究所 | Truck wheel axle identification method and system based on image processing |
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